Related Experiment Videos
An autoregressive repeatability animal model for test-day records in multiple lactations
J Carvalheira1, E J Pollak, R L Quaas
1Instituto de Ciêncas Blomédlcas Abel Salazar and Centro de Estudos de Ciência Animal, Universidade do Porto, Vairão, Portugal. jgc3@mail.icav.up.pt
Journal of Dairy Science
|September 7, 2002
Summary
A new autoregressive repeatability model improves genetic evaluation for dairy cattle by accurately accounting for environmental influences across multiple lactations. This method enhances milk production predictions compared to traditional approaches.
Area of Science:
- Animal Genetics
- Dairy Science
- Statistical Modeling
Background:
- Test-day (TD) models are standard for dairy cattle genetic evaluation.
- Existing models face challenges with parameter estimation and accurately correlating repeated milk yield records within and across lactations.
Purpose of the Study:
- To define and evaluate a multiple-lactation autoregressive-repeatability model.
- To disentangle environmental effects within and between lactations for improved accuracy.
Main Methods:
- Developed and tested a multiple-lactation autoregressive-repeatability animal model.
- Used simulated records with and without long-term environmental effects.
- Employed DFREML-simplex methodology for variance component estimation.
Main Results:
- The autoregressive TD animal model accurately detected the presence and absence of long-term environmental effects.
- Variance components and correlations were accurately recovered with 10 parameters for three lactations.
- The model effectively reduced residual variance components.
Conclusions:
- Autoregressive animal models are a superior alternative to classical methods for dairy cattle genetic evaluation.
- This approach improves the accuracy of predicting milk production.
- The model offers a more precise method for understanding environmental influences on yield.